WO2006000789A1 - Procede de correlation du mouvement de tissus internes - Google Patents

Procede de correlation du mouvement de tissus internes Download PDF

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Publication number
WO2006000789A1
WO2006000789A1 PCT/GB2005/002493 GB2005002493W WO2006000789A1 WO 2006000789 A1 WO2006000789 A1 WO 2006000789A1 GB 2005002493 W GB2005002493 W GB 2005002493W WO 2006000789 A1 WO2006000789 A1 WO 2006000789A1
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WO
WIPO (PCT)
Prior art keywords
movement
internal tissue
points
surface movement
tissue movement
Prior art date
Application number
PCT/GB2005/002493
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English (en)
Inventor
Guang-Zhong Yang
Nicholas A. Ablitt
David N. Firmin
Original Assignee
Imperial Innovations Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Imperial Innovations Ltd. filed Critical Imperial Innovations Ltd.
Priority to US11/630,717 priority Critical patent/US20080300502A1/en
Priority to EP05755129A priority patent/EP1761168A1/fr
Publication of WO2006000789A1 publication Critical patent/WO2006000789A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A61B5/1135—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing by monitoring thoracic expansion
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0062—Arrangements for scanning
    • A61B5/0064—Body surface scanning
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
    • A61B5/1127—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique using markers

Definitions

  • the invention relates to a method of correlating internal tissue movement for example for deriving respiratory induced cardiac deformation.
  • a significant problem with existing tissue imaging techniques for example in a human patient arises from involuntary acyclic motion. Such motion can be induced by the patient breathing which can compromise the imaging techniques because of the resultant movement or deformation of the tissue being imaged.
  • Various cardiac imaging techniques are known including Positron Emission Tomography (PET), 3D Echo Cardiography and Cardiovascular Magnetic Resonance (MR) techniques and in all of these respiratory induced cardiac deformation is a significant and limiting factor especially at high resolutions when it is desired to image vessel walls and coronary arteries.
  • PET Positron Emission Tomography
  • MR Cardiovascular Magnetic Resonance
  • Cross-modal imaging techniques can give rise to difficulties because of incompatibilities with the respective apparatuses required - for example cardiovascular MR imaging can be compromised if additional metallic objects are in the vicinity.
  • respiratory gating is used.
  • the patient's breathing pattern is monitored and data is filtered so as to exclude data during breathing movement.
  • One particular approach incorporates a navigator echo in which a column of material perpendicular to the respiratory motion has a read-out gradient giving its position allowing a decision to be made on which data should be retained.
  • This technique can be incorporated, for example, with cardiovascular MR as discussed in Ehman RL, Felmlee JP. "Adaptive technique for high-definition MR imaging of moving structures", Radiology. 1989;173(l):255-263.
  • a further proposed solution is to obtain a measure of movement of the patient's chest by measuring its expansion. This is achieved by strapping a bellows-type arrangement around the user's chest and measuring the movement of or strain on a point on the bellows.
  • a problem with this approach is that the surface distortion is poorly coupled to the induced cardiac motion such that the technique is highly inaccurate.
  • PLSR Partial Least Squares Regression
  • Fig. 1 is a diagramatic representation of an apparatus according to the invention
  • Fig. 2 is a flow diagram showing operation of the invention
  • Fig. 3 is a diagram showing implementation of the method.
  • the method according to the invention correlates simultaneous measurements of three dimensional heart movement and two dimensional chest surface (wall) movement. A relationship between these two factors is then extracted using partial least squares regression (PLSR) to provide a mapping of two dimensional chest wall movements to predicted three dimensional heart movement.
  • PLSR partial least squares regression
  • the correlation model hence obtained is derived in a calibration stage on a patient.
  • easily measurable 2D chest surface movement can be obtained and 3D cardiac motion predicted using the mapping allowing tracking of movement of the internal anatomical region of interest.
  • FIG. 1 An apparatus appropriate for carrying out the technique is shown in Fig. 1.
  • a two dimensional chest surface measurement tension jacket 10 detects displacement at the chest surface at a plurality of points 12 and outputs the displacement data to a processor 14 via a bus 15.
  • a cardiovascular MR array 16 simultaneously obtains a dynamic 3D MR image of the heart and outputs the image to processor 14.
  • Processor 14 constructs the image of the spatio- temporal deformation of the heart and correlates the movement to the measured 2D chest surface movements using PLSR during the calibration phase.
  • the wearer of the jacket 10 undergoes a procedure such as a radiotherapy operation in which radiotherapy is carried out by an apparatus as shown generally at 18.
  • the processor 14 controls the radiotherapy beam dependent on respiratory induced cardiac deformation for example in order to avoid irradiating cardiac tissue temporarily obscuring the area on which therapy is being carried out.
  • the cardiac deformation is predicted or modelled by the processor 14 based on the 2D surface measurements simultaneously obtained from the tension jacket 10, using the correlation mapping obtained during the calibration phase.
  • the calibration and prediction phases are carried out immediately one after the other.
  • the prediction phase can be used to remove blurring of 3D imaging due to respiratory motion.
  • the patient undergoes further 3D scanning which may be the same or a different modality than that used to capture the 3D information during the training phase.
  • the captured 3D images can be corrected using the predicted 3D motion derived from the readings from the tension jacket.
  • the approach can be implemented for motion checking during imaging or therapy to compensate for motion- induced artefacts and degradation such as respiratory induced blurring.
  • the invention can be further understood with respect to the flow diagram shown in Fig. 2.
  • the three dimensional imaging step is carried out using cardiovascular MR.
  • modelling of the imaged data and registration to a selected reference volume is carried out to obtain a three dimensional spatio-temporal image effectively reflecting the respiration induced deformation of the heart overtime against the selected reference volume.
  • the modelled image is correlated with real time measured surface inputs at block 34 and a prediction model is derived from the correlation at block 36.
  • real time measured surface inputs from block 34 are input to block 38 to provide imaging with real time tracking and adaptation for cardiac movement.
  • intrinsic motion sensitivity to modelling of the imaging process is carried out and input to imaging block 38 allowing adjustment of scanning parameters on the fly depending on the information derived from the motor modelling.
  • the tension jacket 10 shown in Fig. 1 can be any appropriate garment incorporating multiple strain and/or curvature or bend sensors as will be well known to the skilled reader, for example optical, ultrasonic, tension or pressure sensors which are compatible with the 3D imaging modality.
  • multiple optically readable points whose displacement can be measured by a remote sensor for example of the type manufactured under the name "NDI Polaris" by Northern Digital Inc of Ontario, Canada can be used.
  • NDI Polaris by Northern Digital Inc of Ontario, Canada
  • Such a sensor can use infrared light to avoid interference from, for example, bright surgical lights.
  • the optically readable points can for example be in the form of barcodes allowing additional data to be derived. Of course any surface movement tracking arrangement can be adopted.
  • optically readable indicia can be painted or adhered or otherwise formed directly on the patient's skin, or displacement or strain sensors can be provided on a belt or array worn by the patient.
  • the sensed data provides a direct reading of the displacement of each point on the chest surface of the patient which is particularly advantageous as the data can be used with minimal processing as a representation of the chest movement during both the calibration and subsequent prediction phases.
  • an optical fibre sensor may be used for motion and/or curvature measurement.
  • Such a sensor is described in "Evaluation of a novel plastic optical fibre sensor for axial strain and bend measurements", K S C Kuang.W K Cantwell, and P J Scully, Meas.Sci. Technol. 13 (2202) 1523- 1534, incorporated herein by reference.
  • such a sensor includes one or more optical fibres, for example a plastic optical fibre, with a light source at one end and a detector at the other end.
  • the fibre includes a portion of pre-determined lengths in which a segment of the cross section of the fibre is removed, for example by abraiding the surface of the fibre with a razor blade.
  • a segment of the cross section of the fibre is removed, for example by abraiding the surface of the fibre with a razor blade.
  • the optical fibre sensors may be used in short length at the plurality of points 12.
  • long fibres may be incorporated from one side of the chest to the other and from top to bottom of the chest such that global curvature of the chest can be detected.
  • the MR scanner 16 can be any appropriate scanner for example a Siemens Sonata MR scanner available from Siemens, Germany. Any other appropriate cardiac scanning/imaging device can alternatively be used. Similarly any appropriate processor 14 and supporting software can be adopted to implement the PLSR correlation approach described in more detail below.
  • Nonrigid registration using free-form deformations application to breast MR images. IEEE Trans Med Imaging. 1999; 18(8): 712-721 is used.
  • a hierarchical transformation model of soft tissue deformation is employed, in which the global motion of the heart is modelled by an affme transformation whereas local deformation is described by free-form deformation based on B-splines applied to a volumetric mesh of control points overlaid on the 3D image.
  • Normalised Mutual Information (Studholme C, Hawkes DJ, Hill DLG, A Normalised Entropy Measure of 3D Medical Image Analysis.
  • the algorithm works by decoupling global and local motion such that only the affme transformation parameters are optimised initially. This is then followed by optimising the non-affme transformation parameters at increasing levels of resolution of the control point mesh.
  • the final number of control points used is 9x9x9 to cover the image volume, which gives the total degrees- of-freedom of 2187.
  • the PLSR technique used to correlate the 3D heart data with the 2D chest surface data will be generally well known to the skilled reader and the basic technique is described in Wold, H. "Soft modelling with latent variables: the nonlinear iterative partial least squares approach”. Perspectives in probability and Statistics: Papers in honor of M.S. Barlett, (J Gani, ed). London: Academic Press. 1975: 114-142.
  • a particular benefit of PLSR is that it is designed to extract intrinsic relationships between data sets. Its ability to extract correlations between input and output data that is itself highly collinear, allows it to deal with problems that would be inappropriate for multi linear or principal components regression. For completeness a treatment of the implementation of PLSR to obtain the correlation model of the present invention will now be described.
  • PLSR regression finds components from X that are also relevant for Y.
  • PLSR searches for a set of components called latent vectors that performs a simultaneous decomposition of X and Y with the constraint that these components explain as much as possible of the covariance between X and Y.
  • latent vectors that performs a simultaneous decomposition of X and Y with the constraint that these components explain as much as possible of the covariance between X and Y.
  • PCR Principal Components Regression
  • the direction in the space of X is sought, which yields the biggest covariance between X and Y. The method examines both the X and Y data and extracts the factors that are significant to both of them.
  • the factors extracted are in order of significance, by evaluating X T Y, to obtain the primary factor with which X determines the variation in Y. Accordingly, applying PLSR, and assuming that the dimension used to describe the distribution of myocardial deformation (response) is q (for example 729 in the case of a 9x9x9 grid of control points) and the dimension used to describe each surface measurement for the respiratory motion is (predictor) p, when a total number of m experiments are performed to extract the relationship between X and Y, the size of the matrices will be mxp and mxq for X and Y, respectively. With PLSR, both the predictor and response matrices are decomposed, such that
  • T and U are latent variable between which PLSR seeks to find an inner relationship and E and F are factors in X and Y that are not described by the PLSR model T comprising a factor score matrix, P the factor loading matrix and Q the coefficient loading matrix.
  • X c and Y c represent the mean centred matrices of X and Y, respectively.
  • PLSR tries to find a score vector t (column of T) in the column space of X c and a score vector u (column of U) in the column space of Y c such that
  • the method searches for a set of latent vectors that performs a simultaneous decomposition of X and Y with the constraint that these components explain as much as possible of the covariance between X and Y.
  • the method tries to establish the inner relationships between the latent variables T and U, derived from X and Y in equations (1) and (2) respectively.
  • Y c TBQ a (7)
  • the values of B and Q are obtained from equation (1) to (6) and the value of T is obtained from the measured value of X c and equation (1).
  • NIPAS non-linear iterative partial least squares
  • 3D anatomical data of the target anatomy in response to motion needs to be acquired.
  • This can be achieved by using any anatomical imaging techniques such as CT or MRI.
  • this is achieved by using MR imaging which is carried out on a Siemens Sonata MR scanner having a field strength of 1.5T, a peak gradient strength of 40mT/m and a slew rate of 200mT/ms. All images are acquired in the supine position and oversampled 3D datasets as discussed in Keegan J, Gatehouse PD, Yang GZ, Firmin DN.
  • Coronary artery motion with the respiratory cycle during breath- holding and free-breathing implications for slice-followed coronary artery imaging.
  • the duration of the examination is about 20 to 25 minutes, depending on the heart rate.
  • the imaging parameters used include an EF flip angle of 65°, in plane matrix size of 256x102, pixel size of 1.56x2.70mm, and field of view (FOV) of 400x275mm.
  • the 3D slab comprises 14 slices, covered by two segments with 51 views per segment. This gave a total of 28 segments per 3D slab. Data acquisition is repeated 20 times for a total acquisition duration of 560 cardiac cycles. Data is acquired with four receiver coils. All raw data, is stored and processed off-line.
  • Image sets are then created from the raw data by using the 3D FFT. Contributions from all coils are combined with an equal weight. Image sets can be created for between six and seven different respiratory positions covering from end-inspiration to end expiration. In general, any MR pulse sequence that gives 3D coverage of the target anatomy at given motion position can be used for this invention.
  • the approach described herein provides numerous advantages. Cross modality reconstruction of patients specific models for dense motion field prediction are allowed which, after initial modelling, can be used in real-time prospective motion tracking or correction. As a result of the technique described above a large number of predictor variables can be used even when the principal modes of variation of the response (cardiac motion) variables are limited.
  • the strength of the PLSR approach is that it additional permits reliable motion prediction when the number of observations is significantly less than the observed variables.
  • the surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation they can be used to accurately predict cardiac motion through the extraction of the latent variables of both the input and output of the model. It is particularly useful when the data involved is highly collinear as the approach accounts for redundancies in both the predictor (surface measurement) and response (cardiac motion).
  • the approach can be used to remove blurring due to respiratory motion.
  • the approach can be applied to any organ, tissue or visceral/anatomical structure and can be used to correlate the motion of any appropriate part of a body surface.
  • the technique can be used for any living matter such as humans or animals.
  • Any manner of obtaining movement data and correlating it can be adopted.
  • registration based on free-form deformation (FFD) or finite element modelling (FEM) can be used to recover the underlying spatio-temporal deformation of the anatomical structure.
  • FFD free-form deformation
  • FEM finite element modelling
  • non-linear and kernel based PLSR approaches may be used of the type described in Malthouse E, Tamhane A, Mah R. "Nonlinear partial least squares". Computers in Chemical Engineering. 1997; 21(8): 875-890.
  • the 3D motion prediction technique can be used on motion tracked imaging in MR as well as for other parallel imaging modalities such as PET, Computer Tomography (CT) or 3D Echo Cardiography and the delivery of focused imaging in the presence of physiological motion.
  • Parallel imaging can be adopted to reduce imaging time.
  • surface tension arrays or optical approaches has been discussed, other techniques based on strain or surface position, or ultrasound based techniques can be used.
  • micro- sensors Yet a further possibility is the use of micro- sensors.
  • chest intensity profiles can be used as a means of measuring local surface deformation.
  • the techniques adopted are used within the constraints of modality compatability for example for MR in which the exclusion of ferromagnetic materials and the restriction of RF are of significant imporance.
  • the techniques described can be used to support any appropriate application such as medical or diagnostic procedures in which the management of inconsistent physiological motion is required, such as motion tracking, calibration and detection.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Heart & Thoracic Surgery (AREA)
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Abstract

Selon l'invention, le mouvement de tissus internes est corrélé avec le mouvement de la surface corporelle externe par suivi du mouvement de surface externe au niveau de plusieurs points superficiels (12) par imagerie simultanée du mouvement des tissus internes et corrélation du mouvement externe et interne au moyen d'une régression partielle par la méthode des moindres carrés en vue d'obtenir un modèle de corrélation. Dans des techniques ultérieures, le mouvement des tissus internes peut être prédit à partir du mouvement superficiel externe en utilisant le modèle de corrélation.
PCT/GB2005/002493 2004-06-25 2005-06-23 Procede de correlation du mouvement de tissus internes WO2006000789A1 (fr)

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US11/630,717 US20080300502A1 (en) 2004-06-25 2005-06-23 Method Of Correlating Internal Tissue Movement
EP05755129A EP1761168A1 (fr) 2004-06-25 2005-06-23 Procede de correlation du mouvement de tissus internes

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GB0414328.5 2004-06-25
GBGB0414328.5A GB0414328D0 (en) 2004-06-25 2004-06-25 Method of correlating internal tissue movement

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Cited By (3)

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US20130303898A1 (en) * 2012-05-09 2013-11-14 Paul E. Kinahan Respiratory motion correction with internal-external motion correlation, and associated systems and methods
WO2017036774A1 (fr) * 2015-08-28 2017-03-09 Koninklijke Philips N.V. Appareil permettant la détermination d'une relation de mouvement
WO2019167721A1 (fr) * 2018-03-02 2019-09-06 株式会社豊田中央研究所 Procédé d'estimation d'informations corporelles internes, programme d'ordinateur, support d'informations contenant ce dernier, et dispositif d'estimation d'informations corporelles internes

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US9076227B2 (en) * 2012-10-01 2015-07-07 Mitsubishi Electric Research Laboratories, Inc. 3D object tracking in multiple 2D sequences
US9635895B1 (en) 2013-10-29 2017-05-02 Vf Imagewear, Inc. System and method for mapping wearer mobility for clothing design
EP3671660A1 (fr) * 2018-12-20 2020-06-24 Dassault Systèmes Conception d'un objet modélisé 3d par l'intermédiaire d'une interaction de l'utilisateur

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130303898A1 (en) * 2012-05-09 2013-11-14 Paul E. Kinahan Respiratory motion correction with internal-external motion correlation, and associated systems and methods
US9451926B2 (en) * 2012-05-09 2016-09-27 University Of Washington Through Its Center For Commercialization Respiratory motion correction with internal-external motion correlation, and associated systems and methods
WO2017036774A1 (fr) * 2015-08-28 2017-03-09 Koninklijke Philips N.V. Appareil permettant la détermination d'une relation de mouvement
US11116582B2 (en) 2015-08-28 2021-09-14 Koninklijke Philips N.V. Apparatus for determining a motion relation
WO2019167721A1 (fr) * 2018-03-02 2019-09-06 株式会社豊田中央研究所 Procédé d'estimation d'informations corporelles internes, programme d'ordinateur, support d'informations contenant ce dernier, et dispositif d'estimation d'informations corporelles internes
JP2019150332A (ja) * 2018-03-02 2019-09-12 株式会社豊田中央研究所 身体内部情報推定方法、コンピュータプログラム、それを記憶した記憶媒体、および、身体内部情報推定装置

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